The choice of data distribution and optimization algorithm will further reduce the scope of the hypothesis space, thereby affecting the expression capacity.

For example, Rademacher complexity is a data-dependent expressive capacity measure taking into account the effect of data distribution [1]}, [2]}. However, to the best of our knowledge, the effect of data complexity and optimization process on expressive capacity of deep learning models is still rarely explored.
